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DIRECT WHOLEBODY PATLAK AND LOGAN IMAGE ESTIMATION FROM
LISTMODE PET DATA
by
Wentao Zhu
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2014
Copyright 2014 Wentao Zhu

We investigate using list‐mode PET data to perform Patlak and Logan modeling. We first propose an estimation method for irreversible tracers (where Patlak modeling is applied) for dynamic PET studies in which we compute voxel‐wise estimates of Patlak parameters using two frames of data. Our approach directly uses list‐mode arrival time for each event to estimate the Patlak parametric image. We use a penalized likelihood method in which the penalty function uses spatially variant weighting to ensure a count independent local impulse response. We evaluate performance of the method in comparison to fractional changes in standardized uptake value (%DSUV) between the two frames using Cramer‐Rao analysis and Monte Carlo simulation. Receiver operating characteristic (ROC) curves are used to compare performance in differentiating tumors from background based on the dynamic data sets. Using area under the ROC curve as a performance metric, we show superior performance of Patlak compared to %DSUV over a wide range of dynamic data sets and parameters. These results suggest that Patlak analysis can be used for dynamic dual‐frame PET data and may lead to better quantitative results relative to %DSUV method. ❧ We then extend our method to clinical wholebody patient data with ¹⁸F‐FDG, which is a commonly used irreversible tracer for oncologic applications. Several practical issues such as estimation of dynamic randoms and scatters rate functions, motion correction, and the estimation of blood input function are discussed and effectively handled. ❧ A new whole‐body PET scan protocol is proposed, where the patient is stepped through the PET scanner twice, after which the Patlak parametric image for each bed position is estimated from the listmode data of the two frames and stitched together to form a wholebody Patlak parametric image. Ten patients were scanned using this protocol. We then draw liver, muscle, and spine regions of interest (ROIs) for these patients and the mean and standard deviation (s.d.) for the Patlak slope (net influx rate) and intercept are computed. We also compute the SUV values and the s.d. for the same ROIs at the 2 corresponding frames time, after which the fractional SUV (%DSUV) is calculated. Results show that Patlak method has less inter-subject variation and higher tumor‐to‐background contrast. Overall, the Patlak estimates provide quantitative parameters better describing tracer uptake than SUV. This may be useful for cancer staging, treatment planning and assessing response to therapy. ❧ We also investigate the direct estimation of Logan parameters (where a reversible tracer is used) from listmode PET data. The ill‐conditioning nature of listmode reconstruction for PET data from a reversible radioactive tracer is explored and discussed. We derive a modified scheme improving the conditioning of the problem by utilizing data before the steady state. This method differs from a previously proposed sinogram based reconstruction methods by other researchers in that it follows the true likelihood of the data. Cramer‐Rao analysis and Monte Carlo simulation demonstrate the effectiveness of our method. ❧ Nowadays many PET/CT scans are performed with contrast agents aiming at enhancing soft tissue contrast in CT images. The contrast agent will increase the CT value significantly especially in the blood rich soft tissues such as liver and heart. However the liner attenuation coefficient of contrast agent is only slightly higher than water at 511 keV. As a result, using the same mapping from low to high energies for non‐contrast CT will result in over‐estimation of attenuation in regions contains contrast region for PET. ❧ This leads to inaccuracies in PET images. We describes a joint estimation algorithm for emission and attenuation maps using contrast enhanced CT as a prior. A comparison with simple rescaling scheme shows that our method can improve quantitative image accuracy when using CT with contrast to compute attenuation coefficients.

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DIRECT WHOLEBODY PATLAK AND LOGAN IMAGE ESTIMATION FROM
LISTMODE PET DATA
by
Wentao Zhu
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2014
Copyright 2014 Wentao Zhu